2 citations · 5 across the 9 of their papers we have counts for
12 papers
AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference
Hangfeng Liang, Yutao Hu, Yanhan Hu +3
Low-light video enhancement (LLVE) remains a challenging task due to severe information degradation under low-illumination conditions. Recent multimodal approaches have significant…
Impostor: An Agent-Curated Benchmark for Realistic AIGC Manipulation Localization
Zhenliang Li, Yutao Hu, Qixiong Wang +5
Recent advances in generative image editing have improved the realism and controllability of localized image manipulation, raising new challenges for image manipulation detection a…
Cardiac-CLIP: A Vision-Language Foundation Model for 3D Cardiac CT Images
Yutao Hu, Ying Zheng, Shumei Miao +20
Foundation models have demonstrated remarkable potential in medical domain. However, their application to complex cardiovascular diagnostics remains underexplored. In this paper, w…
Learning Dense Feature Matching via Lifting Single 2D Image to 3D Space
Yingping Liang, Yutao Hu, Wenqi Shao +1
Feature matching plays a fundamental role in many computer vision tasks, yet existing methods heavily rely on scarce and clean multi-view image collections, which constrains their…
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather
Yuran Wang, Yingping Liang, Yutao Hu +1
Learning-based stereo matching models struggle in adverse weather conditions due to the scarcity of corresponding training data and the challenges in extracting discriminative feat…
Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images
Yingping Liang, Ying Fu, Yutao Hu +3
Optical flow estimation is a crucial subfield of computer vision, serving as a foundation for video tasks. However, the real-world robustness is limited by animated synthetic datas…
Distilling Monocular Foundation Model for Fine-grained Depth Completion
Yingping Liang, Yutao Hu, Wenqi Shao +1
Depth completion involves predicting dense depth maps from sparse LiDAR inputs. However, sparse depth annotations from sensors limit the availability of dense supervision, which is…
Car-1000: A New Large Scale Fine-Grained Visual Categorization Dataset
Yutao Hu, Sen Li, Jincheng Yan +2
Fine-grained visual categorization (FGVC) is a challenging but significant task in computer vision, which aims to recognize different sub-categories of birds, cars, airplanes, etc.…
StructDiff: Structure-aware Diffusion Model for 3D Fine-grained Medical Image Synthesis
Jiahao Xia, Yutao Hu, Yaolei Qi +6
Solving medical imaging data scarcity through semantic image generation has attracted growing attention in recent years. However, existing generative models mainly focus on synthes…
EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation
Siyuan Huang, Liliang Chen, Pengfei Zhou +8
We introduce EnerVerse, a generative robotics foundation model that constructs and interprets embodied spaces. EnerVerse employs a chunk-wise autoregressive video diffusion framewo…
OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM
Yutao Hu, Tianbin Li, Quanfeng Lu +4
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in various multimodal tasks. However, their potential in the medical domain remains largely unexplore…
Beyond One-to-One: Rethinking the Referring Image Segmentation
Yutao Hu, Qixiong Wang, Wenqi Shao +4
Referring image segmentation aims to segment the target object referred by a natural language expression. However, previous methods rely on the strong assumption that one sentence…